Data-Driven Proactive Early Warning of Grid Congestion Probability Based on Multiple Time Scales

With the development of new power systems, the increased interactive demand on the load side, and the high proportion of renewable energy sources on the power side, grid congestion problems due to increased system uncertainty are becoming more frequent. In this context, grid congestion problems have become more and more frequent. In order to solve the problem of a lack of accuracy and predictability of the current scheduling method based on “passive” prediction, a data-driven active warning method based on the probability of grid congestion at multiple time scales is proposed. First, a multi-stage joint optimization feature selection model is constructed to capture the 12 feature sets that are most conducive to grid congestion warning from the massive grid history data containing 622 features. Then, a multi-time-scale prediction model based on a convolutional neural network and a bi-directional long and short-term memory network is constructed to realize the active early warning of the power system in the face of grid congestion events. Finally, the proposed method and model are verified with the actual operation data of the power grid in a province in China, and the computational results verify that the proposed method and model can realize the active early warning, which can help the dispatchers sense the development of grid congestion in advance and take control measures in time.